Quantitative indicators for environmental and social sustainability performance assessment of the supply chain.
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| Title: | Quantitative indicators for environmental and social sustainability performance assessment of the supply chain. |
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| Authors: | Okay, Nilufer C.1 (AUTHOR) niluferokay@gmail.com, Sencer, Asli1 (AUTHOR) asli.sencer@bogazici.edu.tr, Taskin, Nazim1 (AUTHOR) nazim.taskin@bogazici.edu.tr |
| Source: | Environment, Development & Sustainability. Mar2026, Vol. 28 Issue 3, p6049-6069. 21p. |
| Subject Terms: | *Environmental indicators, Social indicators, Supply chain management, Government accountability, Industry 4.0, Social impact assessment |
| Company/Entity: | Global Reporting Initiative (Organization) |
| Abstract: | In the era of globalization, supply chains are becoming less transparent, facing pressing sustainability challenges such as the inappropriate use of natural resources, poor working conditions, and environmental degradation. This paper addresses these issues by presenting a pioneering sustainability assessment framework aimed at increasing transparency and accountability in global supply chains. Emerging from a systematic literature review and insights from the Global Reporting Initiative (GRI), the framework comprises 91 robust performance indicators: 36 environmental and 55 social. These indicators, a mix of quantitative and semi-quantitative measures, provide a comprehensive tool for assessing the sustainability performance of supply chain actors across a range of sectors. The framework not only facilitates companies in measuring their own and their suppliers' sustainability performance but also enhances their capacity to effectively communicate their environmental and social progress to stakeholders. Additionally, it is designed to seamlessly integrate with Industry 4.0 technologies, enabling more dynamic assessments. [ABSTRACT FROM AUTHOR] |
| Copyright of Environment, Development & Sustainability is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
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